English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Plus annual

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Plus monthly

Global: Zendy Tools annual

Global: Zendy Tools monthly

Global: Zendy Free Plan

Deep neural networks have shown promising results in image inpainting even ifthe missing area is relatively large. However, most of the existing inpaintingnetworks introduce undesired artifacts and noise to the repaired regions. Tosolve this problem, we present a novel framework which consists of two stackedconvolutional neural networks that inpaint the image and remove the artifacts,respectively. The first network considers the global structure of the damagedimage and coarsely fills the blank area. Then the second network modifies therepaired image to cancel the noise introduced by the first network. Theproposed framework splits the problem into two distinct partitions that can beoptimized separately, therefore it can be applied to any inpainting algorithmby changing the first network. Second stage in our framework which aims atpolishing the inpainted images can be treated as a denoising problem where awide range of algorithms can be employed. Our results demonstrate that theproposed framework achieves significant improvement on both visual andquantitative evaluations.

Deep Stacked Networks with Residual Polishing for Image Inpainting